In large enterprise businesses, such as a financial institution having offices and branches located worldwide, data management and validation can be a daunting task, since the data originates from various sources and is implementing in various systems. For example, the enterprise may have multiple divisions, entities, product lines and the like, each with their own systems of record, data sources/extracts, databases and the like.
In instances in which an enterprise/large business has disparate systems of record, data sources/extracts, databases and the like, data research and validation becomes a time consuming and inefficient task because information is stored at various locations and users have to access various systems, documents and the like to perform such data research and/or validation tasks. Thus, the ability to readily generate reports, such as reports that map data fields from different data sources and the like, is severely impacted.
Moreover, data analysis and searching becomes problematic because a data field originating from one data source or stored in one data system/database may have a different label/name when originating from another data source or when stored in another data system/database. In addition, the manual aspect of such data analysis and validation is unreliable since the resulting mapping of data fields from table-to-table, database-to-database and the like is prone to human error/inaccuracies.
In the financial institution scenario, when undertaking a data research activity it is important to understand and validate data connections across all systems and applications. The absence of systematic information on data lineage and mapping results in difficulty in validating calculation logic and, as such, the downstream impact on validation of balance sheets and income statements is negatively affected.
Therefore, a need exists to develop systems, apparatus, computer program products, methods and the like that facilitates data research and validation amongst disparate data systems, data sources and the like. The desired systems, apparatus and the like should provide the requisite linkage and/or mapping of data fields from amongst multiple data sources and/or data systems. In addition, a need exists to provide such linkage and mapping information in a comprehensive platform that is easily accessible, user-friendly and provides flexibility and efficiency in generating reports using information from multiple different data sources, databases, tables and the like. Moreover, the desired systems, apparatus and the like should provide the ability to readily analyze questions and issues surrounding data by providing upstream data source(s) (i.e., the secondary data fields used to calculate a selected data field), downstream data impact (i.e., where is the data used in calculating other data fields) and the business rules impacted by the data.